# The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification

arXiv: https://arxiv.org/abs/2010.09381 · HTML: https://mlsanity.com/paper/2010.09381/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 13 of 13)

- [Relational World Knowledge Representation in Contextual Language Models: A Review](https://mlsanity.com/paper/2104.05837/) (2021)
- [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://mlsanity.com/paper/2110.08151/) (2021)
- [Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples](https://mlsanity.com/paper/2111.10962/) (2021)
- [DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2104.08655/) (2021)
- [CreoleVal: Multilingual Multitask Benchmarks for Creoles](https://mlsanity.com/paper/2310.19567/) (2023)
- [Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation](https://mlsanity.com/paper/2503.06594/) (2025)
- [Multilingual Relation Classification via Efficient and Effective Prompting](https://mlsanity.com/paper/2210.13838/) (2022)
- [Multilingual Fact Linking](https://mlsanity.com/paper/2109.14364/) (2021)
- [MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset](https://mlsanity.com/paper/2305.04582/) (2023)
- [Adapters for Enhanced Modeling of Multilingual Knowledge and Text](https://mlsanity.com/paper/2210.13617/) (2022)
- [Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction](https://mlsanity.com/paper/2305.10985/) (2023)
- [MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs](https://mlsanity.com/paper/2505.19800/) (2025)
- [Multilingual Entity and Relation Extraction from Unified to Language-specific Training](https://mlsanity.com/paper/2301.04434/) (2023)

## Abstract (arXiv)

Relation classification is one of the key topics in information extraction, which can be used to construct knowledge bases or to provide useful information for question answering. Current approaches for relation classification are mainly focused on the English language and require lots of training data with human annotations. Creating and annotating a large amount of training data for low-resource languages is impractical and expensive. To overcome this issue, we propose two cross-lingual relation classification models: a baseline model based on Multilingual BERT and a new multilingual pretraining setup, which significantly improves the baseline with distant supervision. For evaluation, we introduce a new public benchmark dataset for cross-lingual relation classification in English, French, German, Spanish, and Turkish, called RELX. We also provide the RELX-Distant dataset, which includes hundreds of thousands of sentences with relations from Wikipedia and Wikidata collected by distant supervision for these languages. Our code and data are available at: https://github.com/boun-tabi/RELX